The MultiRes block presents a compact form of Inception block [1]} and incorporates a residual connection for aiding in the gradient flow [2]}. Both inception block [3]} and residual connections [4]} have demonstrated favorable outcome in ECG based biometrics. We place 3 convolutional layers having filters of size 32, 64, and 128, respectively and concatenate their featuremaps, which in essence factorizes larger convolutional operations using smaller ones. Since we are using a shallow network, we consider convolutional filters of size 15, to expand the receptive field. We avoid intermediate pooling operations to prevent loss of information, but after performing the convolutions for the sake of summarization of the featuremaps, max pooling is performed. To exploit the multiresolutional nature of our analysis further, we perform spatial pyramid pooling instead of the vanilla pooling operation. Three pooling operations, with windows of size of 8, 16, and 32 are performed in parallel and the outputs are concatenated. Finally, a fully connected layer with 128 neurons is introduced to generate the embedding for the input signals. This layer is further regularized with a dropout of 0.25 to avoid overfitting [5]}. All the layers used in this network are batch-normalized [6]} and activated by Rectified Linear Unit (ReLU) activation.
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